Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. OATP1B1 (SLCO1B1) and OATP1B3 (SLCO1B3) are `uptake' transporters largely restricted to the sinusoidal aspect of hepatocytes. They both transport a wide variety of structurally-unrelated compounds, including members of several clinically im- portant drug families such as statins, sartans and angiotensin converting enzyme (ACE) inhibitors. We now propose to test over 1000 drugs against 2 substrates for each transporter in vitro. We will then use these data to curate and validate machine learning models. We will also use an array of machine learning methods as well as multiple model evaluation metrics. This will enable us to develop a web-based software tool called MegaTrans that will encourage the user to input their own compound structures and generate predictions for interactions with transporter/s of interest and then visualize the similarity to the training set of each model using several different visualization methods. The return on investment of such a tool would be that it could assist in the design and selection of more favorable compounds that avoid transporters of interest while also saving time and money. It could also identify compounds that are already approved that might present a drug interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In Phase II we would greatly expand the number of transporters which we would generate data on and build models such that we could address all the major transporters of interest to drug discovery.